ABSTRACT
Since the post-COVID-19 era, online teaching has become the normalization of universities in China. However, it is still unclear whether students are satisfied with online teaching and what factors affect online learning satisfaction of university students. Therefore, based on the establishment of basic hypothesis, through the construction of path model and questionnaire survey, this paper found the online learning satisfaction of university students in the post-COVID-19 era in China and revealed the main factors affecting online learning satisfaction. The results show that university students are satisfied with the overall evaluation and sub-item evaluation of online learning, and students have a positive attitude toward online learning. From the perspective of the influencing path, curriculum design and learning environment have a positive direct impact on the online learning satisfaction, while teacher characteristics have an indirect positive impact on the online learning satisfaction mainly through curriculum design and learning environment. From the perspective of the degree of influencing factors, although teachers' characteristics have indirect influence, the degree of influence is the largest. According to the results, this paper provides some suggestions on the improvement of teachers, curriculum design, and learning environment, to improve the online learning satisfaction of university students. © 2022 ICCREM 2022: Carbon Peak and Neutrality Strategies of the Construction Industry - Proceedings of the International Conference on Construction and Real Estate Management 2022. All rights reserved.
ABSTRACT
In this article, we investigate human-centered artificial intelligence (HCAI) in an educational context where pupils used block-based programming in small groups to solve tasks given by the teacher. We used a design-based research approach in which we, together with the teachers, created a maker space for explorative science learning and organised teaching interventions wherein the pupils met online three hours a week for 16 weeks for an entire school year. Due to COVID-19, data were collected through Zoom, with collaborative learning situations captured through screen sharing and online communication using webcams. We employed three data analysis techniques: interaction analysis, visual artifact analysis, and thematic analysis. We developed an analytical framework for integration using thematic coding that combined concepts from computer-supported collaborative learning (CSCL) and domai-oriented design environments. We report the following findings: 1) Three types of rules between design units were identified with visual artifact analysis: latent, generic, and domai-specific rules;2) two types of CSCL artifacts (technology and discussions) were intertwined and developed in parallel, along with a computer-based scaffolding scenario that offloads domai-specific scaffolding from humans to computers.